Removing quasi-periodic noise in strain maps by filtering in the Fourier domain

International audienceQuasi-periodic noise due to various reasons often corrupts strain maps obtained with full-field measuring systems. The aim of this didactic paper is to show how to remove this noise by changing some Fourier coefficients involved in the two-dimensional (2D) Fourier transform of these strain maps. The basics of the 2D Fourier transform of images, which is a common tool in image processing but that is only scarcely employed in the experimental mechanics community, are first briefly recalled. Several procedures employed for removing undesirable frequencies in strain maps are then discussed. Three different examples illustrate the benefit of this approach

A non-local dual-domain approach to cartoon and texture decomposition

International audienceThis paper addresses the problem of cartoon and texture decomposition. Microtextures being characterized by their power spectrum, we propose to extract cartoon and texture components from the information provided by the power spectrum of image patches. Thecontribution of texture to the spectrum of a patch is detected as statistically significant spectral components with respect to a nullhypothesis modeling the power spectrum of a non-textured patch. The null-hypothesis model is built upon a coarse cartoon representationobtained by a basic yet fast filtering algorithm of the literature. Hence the term ``dual domain'': the coarse decomposition is obtained in thespatial domain and is an input of the proposed spectral approach. The statistical model is also built upon the power spectrum of patches with similar textures across the image. The proposed approach therefore falls within the family of non-local methods. Experimental results are shown in various application areas, including canvas pattern removal in fine arts painting, or periodic noise removal in remote sensing imaging

Determining displacement and strain maps immune from aliasing effect with the grid method

International audienceSpatial aliasing may affect methods based on grid image processing to retrieve displacement and strain maps in experimental mechanics. Such methods aim at estimating these maps on the surface of a specimen subjected to a loading test. Aliasing, which is often not noticeable to the naked eye in the grid images, may give spurious fringes in the strain maps. This paper presents an analysis of aliasing in this context and provides the reader with simple guidelines to minimize the effect of aliasing on strain maps extracted from grid images

An a-contrario approach to quasi-periodic noise removal

International audienceImages can be affected by quasi-periodic noise. This undesirable feature manifests itself by spurious repetitive patterns covering the whole image, well localized in the Fourier domain. While notch filtering permits to get rid of this phenomenon , this however requires to first detect the resulting Fourier spikes, and, in particular, to discriminate between noise spikes and spectrum patterns caused by spatially localized textures or repetitive structures. This paper proposes a statistical a-contrario detection of noise spikes in the Fourier domain. A Matlab code is also provided

Modelling and extraction of fundamental frequency in speech signals

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.One of the most important parameters of speech is the fundamental frequency of vibration of voiced sounds. The audio sensation of the fundamental frequency is known as the pitch. Depending on the tonal/non-tonal category of language, the fundamental frequency conveys intonation, pragmatics and meaning. In addition the fundamental frequency and intonation carry speaker gender, age, identity, speaking style and emotional state. Accurate estimation of the fundamental frequency is critically important for functioning of speech processing applications such as speech coding, speech recognition, speech synthesis and voice morphing. This thesis makes contributions to the development of accurate pitch estimation research in three distinct ways: (1) an investigation of the impact of the window length on pitch estimation error, (2) an investigation of the use of the higher order moments and (3) an investigation of an analysis-synthesis method for selection of the best pitch value among N proposed candidates. Experimental evaluations show that the length of the speech window has a major impact on the accuracy of pitch estimation. Depending on the similarity criteria and the order of the statistical moment a window length of 37 to 80 ms gives the least error. In order to avoid excessive delay as a consequence of using a longer window, a method is proposed ii where the current short window is concatenated with the previous frames to form a longer signal window for pitch extraction. The use of second order and higher order moments, and the magnitude difference function, as the similarity criteria were explored and compared. A novel method of calculation of moments is introduced where the signal is split, i.e. rectified, into positive and negative valued samples. The moments for the positive and negative parts of the signal are computed separately and combined. The new method of calculation of moments from positive and negative parts and the higher order criteria provide competitive results. A challenging issue in pitch estimation is the determination of the best candidate from N extrema of the similarity criteria. The analysis-synthesis method proposed in this thesis selects the pitch candidate that provides the best reproduction (synthesis) of the harmonic spectrum of the original speech. The synthesis method must be such that the distortion increases with the increasing error in the estimate of the fundamental frequency. To this end a new method of spectral synthesis is proposed using an estimate of the spectral envelop and harmonically spaced asymmetric Gaussian pulses as excitation. The N-best method provides consistent reduction in pitch estimation error. The methods described in this thesis result in a significant improvement in the pitch accuracy and outperform the benchmark YIN method

Realistic chipless RFID: identification and localization

Für die weitere Massenverbreitung von RFID Systemen ist ein günstiges und genaues Verfahren zur Objektlokalisierung und –verfolgung zwingend erforderlich. Chiplose RFID Systeme erlauben im Gegensatz zu herkömmlichen chipbehafteten RFID Systemen den Einsatz von einfachen, druckbaren RFID Tags, eine Möglichkeit zum Einstieg in die Ära von extrem billigen RFID Tags. Diese Dissertation konzentriert sich auf die Lösung von drei Herausforderungen bei der Erkennung von chiplosen RFID Tags innerhalb geschlossener Räume. Der erste in der vorliegenden Arbeit diskutierte Aspekt beschäftigt sich mit Methoden zum Eliminieren des Störechos der Umgebung (clutter removal techniques). Im chiplosen RFID System ist das Umgebungsstörecho definiert durch das von der Umgebung reflektierte Signal, das nicht mit dem RFID Tag interagiert. Die Stärke dieses Signals ist in jedem Fall größer als die des vom RFID Tag zurückgestrahlten (backscattered) Signals, was die Signaturerkennung des RFID Tags unmöglich macht. Zur Lösung dieses Problems schlage ich zwei Algorithmen vor. Der erste ist die Leerraum-Kalibrierung (empty room calibration). Bei diesem Algorithmus werden die Messungen mit RFID Tag von denen ohne RFID Tags abgezogen. Der zweite Algorithmus basiert auf dem Rake-Receiver unter Nutzung einer Zufallsfolge (PN sequence), er erfordert keine zusätzliche Kalibrierung. Der zweite Aspekt betrifft die Notch Erkennung und Identifikation, ein sehr wichtiger Bereich des chiplosen RFID Systems. Er ist dafür verantwortlich, die Notchs in Bits umzuwandeln. Für eine effektive Detektion werden Windowing (Fenster) Verfahren vorgeschlagen, wobei jedes Fenster einen oder auch keinen Notch beinhalten kann. Insgesamt drei neue Verfahren zur Notch Erkennung wurden implementiert. Als erstes ein Matched Filter (MF), in dem der einkommende Notch mit einem Referenz Notch verglichen wird. Das zweite Verfahren basiert auf einer gefensterten Singulärwertzerlegung, damit kann sowohl der Notch erkannt werden, als auch seine Bandbreite bestimmt werden. Als drittes Verfahren wird das dynamische Frequency Warping vorgestellt. Diese Technik nutzt nichtlineare um die Notche unddie Frequenzverschiebungen, die an den Notches auftreten, zu erkennen. Als dritter Aspekt wird die Lokalisierung der RFID Tags in dieser Dissertation diskutiert. Dazu werden zwei Algorithmen erklärt und implementiert. Der erste Algorithmus beruht auf der Triangulation durch drei getrennte RFID Lesegeräte, während sich der zweite die Position des RFID Tags aus der Signalstärke und dem Winkel des vom RFID Tag kommenden Signals berechnet. Alle genannten Algorithmen und Verfahren wurden in einer realen Innenraum Testumgebung mit RFID Tags und einer Software Defined Radio (SDR) Plattform vermessen, um die Zuverlässigkeit der Algorithmen unter normalen Bedingungen zu überprüfen.For mass deployment of RFID systems, cheap and accurate item level identification and tracking are profoundly needed. Fortunately, unlike conventional chip-based RFID, chipless RFID systems offers low-cost printable tags holding a better chance to enter the era of penny-cost tags. This dissertation concentrated on solving three challenges in the detection of the chipless tag inside an indoor environment. The first aspect discussed in the thesis are the chipless RFID clutter removal techniques. In chipless RFID the environmental clutter response is defined as the signal reflected from the environment, that does not interact with the tag. This signal has higher power than the backscattered signal from the tag, rendering the tag signature undetectable. Two algorithms to overcome this problem was used, the first is empty room calibration. The first algorithm is based on subtracting the measurement with the tag from the one without. The second algorithm is Rake receiver using PN sequence; this algorithm requires no pre-measurement calibration. The second aspect is notch detection and identification which is a critical part of the chipless system. This part is responsible for converting the notches into bits. For effective detection, a windowing operation is proposed, where each window may contain a notch or not. Three novel techniques are implemented to detect the notch. The first is matched filter were a reference notch is compared with the incoming signal. The second is window based singular value decomposition, where a constellation is created to detect not only the existence of a notch but also the bandwidth of the notch. The third notch detection technique is dynamic frequency warping. This technique utilizes non-linear warping to detect the notch and the frequency shifts that occurs on the notch. The third aspect discussed in the thesis is tag localization. In this aspect, two algorithms are implemented and explained. The first is trilateration which requires three different readers. The second localization algorithm exploits received signal strength and angle of arrival to detect the location of the tag accurately. All the algorithms were tested using a real testbed to validate the reliability of the techniques. The measurements were done using fabricated tags in an indoor environment using Software Defines Radio (SDR)

A guide to LIGO-Virgo detector noise and extraction of transient gravitational-wave signals

The LIGO Scientific Collaboration and the Virgo Collaboration have cataloged eleven confidently detected gravitational-wave events during the first two observing runs of the advanced detector era. All eleven events were consistent with being from well-modeled mergers between compact stellar-mass objects: black holes or neutron stars. The data around the time of each of these events have been made publicly available through the gravitational-wave open science center. The entirety of the gravitational-wave strain data from the first and second observing runs have also now been made publicly available. There is considerable interest among the broad scientific community in understanding the data and methods used in the analyses. In this paper, we provide an overview of the detector noise properties and the data analysis techniques used to detect gravitational-wave signals and infer the source properties. We describe some of the checks that are performed to validate the analyses and results from the observations of gravitational-wave events. We also address concerns that have been raised about various properties of LIGO-Virgo detector noise and the correctness of our analyses as applied to the resulting data

A guide to LIGO-Virgo detector noise and extraction of transient gravitational-wave signals

© 2020 IOP Publishing Ltd. The LIGO Scientific Collaboration and the Virgo Collaboration have cataloged eleven confidently detected gravitational-wave events during the first two observing runs of the advanced detector era. All eleven events were consistent with being from well-modeled mergers between compact stellar-mass objects: black holes or neutron stars. The data around the time of each of these events have been made publicly available through the gravitational-wave open science center. The entirety of the gravitational-wave strain data from the first and second observing runs have also now been made publicly available. There is considerable interest among the broad scientific community in understanding the data and methods used in the analyses. In this paper, we provide an overview of the detector noise properties and the data analysis techniques used to detect gravitational-wave signals and infer the source properties. We describe some of the checks that are performed to validate the analyses and results from the observations of gravitational-wave events. We also address concerns that have been raised about various properties of LIGO-Virgo detector noise and the correctness of our analyses as applied to the resulting data